Description

Problem-solving via search is a fundamental concept in Artificial Intelligence where an agent attempts to find a sequence of actions that leads from an initial state to a goal state. This is achieved by exploring a search space, typically represented as a tree or graph, where each node represents a state and edges represent actions. AI uses various search algorithms—either uninformed (blind) or informed (heuristic)—to efficiently find solutions.

Examples (Code)

Below is a simple example

from collections import deque

# Graph represented as an adjacency list
graph = {
    'A': ['B', 'C'],
    'B': ['D', 'E'],
    'C': ['F'],
    'D': [],
    'E': ['F'],
    'F': []
}

def bfs(start, goal):
    visited = set()
    queue = deque([[start]])

    while queue:
        path = queue.popleft()
        node = path[-1]

        if node == goal:
            return path

        if node not in visited:
            visited.add(node)
            for neighbor in graph.get(node, []):
                new_path = list(path)
                new_path.append(neighbor)
                queue.append(new_path)

print("Path:", bfs('A', 'F'))

Real-World Applications

Where AI Is Applied

Resources

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